Access To Mental Health Care Services Research Articles

Access to Mental Healthcare Services for Black Women during Perinatal Period – A Scoping Review

BACKGROUND: Black women in Canada are at higher risk of poor mental health outcomes; this is associated with disparities such as poor access to healthcare and aggravated by racial discrimination and poor living conditions.
 
 OBJECTIVES: This study aims to investigate the extent and nature of literature on access to mental healthcare services for Black women during the perinatal period in regions outside of Africa and the Caribbean. 
 
 METHODS: We conducted a systematic article search using Medline, Cumulative Index for Nursing and Allied Health Literature (CINAHL); PsycINFO; ProQuest Public Health Database. The search strategy based on the following aspects: 1) Perinatal mental health, specifically maternal mental health 2) People of color, specifically Black people of African descent, or the African diaspora 3) Experiences with mental healthcare, specifically access and utilization. Arksey & O’Malley’s framework for conducting scoping reviews was chosen. The included studies met the following criteria: 1) articles that focused on Black Women during perinatal period living in regions outside of African and the Caribbean, 2) studies with topics related to mental health services among Black women during perinatal period. 
 
 RESULTS: n=12 articles met the inclusion criteria. The following themes were identified from the analysis of literature: 1) Disparities in utilization of mental healthcare services, 2) Spirituality, Faith, and Religion, 3) Accessibility of mental healthcare services. 
 
 CONCLUSION: There is a paucity of evidence about the mental health of prenatal and postnatal Black women living outside Africa and the Caribbean in countries other than the US, the UK, and Canada. 

Artificial Intelligence and Mental Health

In addition to identifying challenges and prospects for such application, this paper intends to present significant implementation science ideas that may be pertinent to comprehending and aiding artificial intelligence (AI) deployment in mental health care. This paper provides an overview of AI technology in modern mental health care and evaluates recent research developments, with a focus on the developing field of digital psychiatry. AI application in psychiatry to improve our knowledge, diagnosis, and treatment of mental diseases; to address concerns with availability, appeal, and accessibility of mental health-care services. Nursing staff members and other medical professionals, who operate in clinical settings, such as mental health facilities, stand to gain significantly from AI’s potential to increase workplace productivity and efficiency. Using the phrases “AI in mental health,” “diagnosis of mental health disorders,” “artificial intelligence,” and “deep learning,” we searched PubMed psychological testing and psychotherapy are being impacted by cutting-edge technologies such as artificial and Web of science. The research covered in this overview of the literature discusses how intelligence. This is a survey article, hence not applicable. The advantages and moral dilemmas associated with the use of AI in psychiatry are outweighed by its potential uses. More research and advancement are needed to get past these obstacles and guarantee the ethical and safe integration of AI in the psychiatric sector. By doing this, AI can significantly advance both long-term outcomes and the organization of mental health services.

Accelerating the impact of artificial intelligence in mental healthcare through implementation science.

The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps regarding how to implement and best use AI to add value to mental healthcare services, providers, and consumers. The aim of this paper is to identify challenges and opportunities for AI use in mental healthcare and to describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. The paper is based on a selective review of articles concerning AI in mental healthcare and implementation science. Research in implementation science has established the importance of considering and planning for implementation from the start, the progression of implementation through different stages, and the appreciation of determinants at multiple levels. Determinant frameworks and implementation theories have been developed to understand and explain how different determinants impact on implementation. AI research should explore the relevance of these determinants for AI implementation. Implementation strategies to support AI implementation must address determinants specific to AI implementation in mental health. There might also be a need to develop new theoretical approaches or augment and recontextualize existing ones. Implementation outcomes may have to be adapted to be relevant in an AI implementation context. Knowledge derived from implementation science could provide an important starting point for research on implementation of AI in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research. However, when taking advantage of the existing knowledge basis, it is important to also be explorative and study AI implementation in health and mental healthcare as a new phenomenon in its own right since implementing AI may differ in various ways from implementing evidence-based practices in terms of what implementation determinants, strategies, and outcomes are most relevant.Plain Language Summary: The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps concerning how to implement and best use AI to add value to mental healthcare services, providers, and consumers. This paper is based on a selective review of articles concerning AI in mental healthcare and implementation science, with the aim to identify challenges and opportunities for the use of AI in mental healthcare and describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. AI offers opportunities for identifying the patients most in need of care or the interventions that might be most appropriate for a given population or individual. AI also offers opportunities for supporting a more reliable diagnosis of psychiatric disorders and ongoing monitoring and tailoring during the course of treatment. However, AI implementation challenges exist at organizational/policy, individual, and technical levels, making it relevant to draw on implementation science knowledge for understanding and facilitating implementation of AI in mental healthcare. Knowledge derived from implementation science could provide an important starting point for research on AI implementation in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research.

Non-suicidal self-injury and its co-occurrence with suicidal behavior: An epidemiological-study among adolescents and young adults.

Non-suicidal self-injury (NSSI) comprising thoughts and behaviors is common and often co-occurring with suicidal behavior like ideation, plan, and attempt. As limited data are available for adolescents and young adults, this study aims to present prevalence estimates for lifetime NSSI, its co-occurrence with suicidal behavior, conditional probabilities and their association with socio-demographic characteristics, severity characteristics of suicidal behavior, and health service utilization. The epidemiological Behavior and Mind Health (BeMIND) study assessed in 2015/16 a random-community sample of N=1180 aged 14-21years from Dresden, Germany, regarding lifetime NSSI via self-administered questionnaire and suicidal behaviors via standardized interview. Any lifetime NSSI was reported by 19.3% (thoughts: 18.0%, behaviors: 13.6%) of the sample with higher prevalence in females (OR=2.7, 95% CI 1.9-3.8, P<0.001). Lifetime prevalence of co-occurring NSSI and suicidal behavior was 7.7%. Females had a 3.3- to 8.8-fold odds of co-occurrence than males. Among those with any NSSI, 39.6% endorsed suicidal behavior, and 66.3% of those with any suicidal behavior reported NSSI. 42.3% of those with any NSSI reported to have used mental healthcare services at any time during their life with higher rates in those with co-occurring suicidal behavior (62.3%). Non-suicidal self-injury and co-occurring suicidal behavior is common in adolescents and young adults-especially females. The limited utilization of mental healthcare services underpins the need for improving recognition of NSSI and suicidal behavior as well as the accessibility of mental healthcare services during adolescence and emerging adulthood.

Implementation of a Psychiatric Nurse-led Outreach Service for Adults Residing at Subvented Old Age Homes: A Hong Kong Experience.

This paper reports on the implementation of a pioneering psychiatric nurse-led service that was designed to address the unmet mental healthcare needs of adults residing at subvented old age homes. It also describes features of potentially wider relevance to nurses interested in developing healthcare service for other underserved populations. We highlight the view that the crux of developing a successful service involves understanding existing service gaps; grasping the pulse of changing healthcare service policies; involving relevant stakeholders in the planning process; validating service outcomes; and seeking support from management. A central goal of the service was to enhance the accessibility of mental healthcare services to the needy. The data was collected over a 5-year period. Our study suggests that this model of service is preferred by service users (both the care providers and the residents in the old age homes); is efficient in terms of providing prompt psychiatric nursing interventions; is able to supply primary care advisors with practical advice in response to enquiries; and helps primary care providers to detect and manage the mental healthcare needs of older adults.

Implementation of a Psychiatric Nurse-Led Outreach Service at Older Adult Homes: A Hong Kong Experience

This article reports on the implementation of a local pioneering psychiatric nurseled service that was designed to address the unmet mental healthcare needs of adults residing at older adult homes. It also describes features of potentially wider relevance to nurses interested in developing healthcare service for other underserved populations. We highlight the view that the crux of developing a successful service involves understanding existing service gaps, grasping the pulse of changing healthcare service policies, involving relevant stakeholders in the planning process, validating service outcomes, and seeking support from management. A central goal of the service was to enhance the accessibility of mental healthcare services to people with unmet needs. This model of service is preferred by service users (both the care providers and the residents in the older adult homes), is efficient in terms of providing prompt psychiatric nursing interventions, is able to supply primary care providers with ractical advice in response to their enquiries, and helps primary care providers to detect and manage the mental healthcare needs of older adults.